Comparison of the analgesic dose of intravenous ketamine versus ketorolac in patients with chest trauma: A randomized double‐blind clinical trial
Bibliographic record
Abstract
BACKGROUND: Pain management is a critical part of treatment in patients with chest trauma. Opioids and nonsteroidal anti-inflammatory drugs have been the most commonly used medications. However, their side effects have drawn attention to other medications. In this study, we aimed to assess the effect of the analgesic dose of ketamine in patients with chest trauma in comparison to ketorolac. METHODS: A randomized, double-blind clinical trial was conducted in three hospitals. Patients were randomly allocated into two groups: 45 in the ketorolac group (30 mg intravenous [IV] and 45 in the ketamine group [0.25 mg/kg IV]). Pain was rated via numeric rating scale (NRS) before and 30 and 60 min after the drug injection. Morphine was used as the rescue medication. Furthermore, the adverse events of the two study regimens were rated. RESULTS: Pain was more significantly relieved in the ketamine group, 30 and 60 min after drug administration, compared to ketorolac (median [IQR] 95% CI 30-min NRS 3.0 [1.0] 2.8-3.5 vs. 5.0 [4.5] 4.2-5.8, p = 0.006; and 60-min NRS 3.0 [2.0] 2.7-3.7 vs. 5.6 [1.7] 4.7-6.4, p < 0.001), respectively. Among patients with a chest tube, pain was more significantly controlled in the ketamine group (p < 0.001). Also, patients in the ketamine group needed less rescue pain medications compared to the ketorolac group although they reported more frequent nausea. CONCLUSION: Ketamine can be an effective analgesic in patients with chest trauma in acute settings with or without rib fracture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".